A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool, Venhound, that has the potential to help investors identify the next unicorn. It is well known that around 90% of startups are unsuccessful: Between 10% and 22% fail within their first year, and this presents a significant risk to venture capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi's The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy.

It's a technique used by many scientific fields, as it provides a structured guideline to answering a question logically and rationally, using empirical evidence -- an approach that ushered humanity out of the dark ages and into today's era where breakthrough discoveries in physics, astronomy and modern medicine are possible. But are there situations in scientific investigation where the scientific method is not needed? A team of researchers at Princeton University's Plasma Physics Laboratory (PPPL) are now proposing that this is indeed possible -- by using a machine learning algorithm that can predict the physical orbits of planets, without the need for it to be based on the laws of physics. The paper on the work, which was recently published in Scientific Reports, outlines how the team trained a machine-learning algorithm on data about the known orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This machine-learning algorithm, paired along with what the team calls a "serving algorithm", was then used to predict the orbits of other planets -- including the parabolic and hyperbolic escaping orbits, of the solar system -- without needing to input Newtonian laws of motion and gravitation. Instead, the approach forms what the team calls a discrete field theory, which models the universe as a kind of "black box."

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirty-Fifth Conference on Artificial Intelligence was held virtually from February 8-9, 2021. There were twenty-six workshops in the program: Affective Content Analysis, AI for Behavior Change, AI for Urban Mobility, Artificial Intelligence Safety, Combating Online Hostile Posts in Regional Languages during Emergency Situations, Commonsense Knowledge Graphs, Content Authoring and Design, Deep Learning on Graphs: Methods and Applications, Designing AI for Telehealth, 9th Dialog System Technology Challenge, Explainable Agency in Artificial Intelligence, Graphs and More Complex Structures for Learning and Reasoning, 5th International Workshop on Health Intelligence, Hybrid Artificial Intelligence, Imagining Post-COVID Education with AI, Knowledge Discovery from Unstructured Data in Financial Services, Learning Network Architecture During Training, Meta-Learning and Co-Hosted Competition, ...

Many people considered it a "formless blur of colors," an image that was abstract but slightly resembling a human face. The image isn't even properly positioned on the canvas, rather it is skewed towards the northwest. In October 2018, this "art piece": Portrait of Edmond de Belamy, an algorithm-generated print, was sold for $432,500, thus beginning the AI-Art goldRush. Humans have always created and enjoyed all forms of art, for viewing purposes, for aesthetic purposes, and even for therapeutic purposes. Since the discoveries of an artistic shell carved by homoerectus, the art business has grown in leaps and bounds and become a highly profitable industry.

Singapore is in the midst of a three-week trial for a pair of autonomous robots that patrol the public for "undesirable social behaviors" that include smoking in prohibited areas and violating COVID-19 gathering regulations. The pair of robots, known as Xavier, are equipped with cameras that can provide 360-degree footage and sensors that allow them to navigate in public and analyze potential public safety violations. According to a press release from the Home Team Science and Technology Agency, if Xavier detects an undesirable behavior, it will alert a public officer control center and officers can respond in person or remotely via the robot's interactive dashboard. Five Singaporean government agencies are involved in the testing of Xavier. "The deployment of ground robots will help to augment our surveillance and enforcement resources," said Lilly Ling, the Singapore Food Agency's East Regional Office Director, in a press release.

For most of the past decade, public concerns about digital technology have focused on the potential abuse of personal data. People were uncomfortable with the way companies could track their movements online, often gathering credit card numbers, addresses, and other critical information. They found it creepy to be followed around the web by ads that had clearly been triggered by their idle searches, and they worried about identity theft and fraud. Those concerns led to the passage of measures in the United States and Europe guaranteeing internet users some level of control over their personal data and images--most notably, the European Union's 2018 General Data Protection Regulation (GDPR). Some argue that curbing it will hamper the economic performance of Europe and the United States relative to less restrictive countries, notably China, whose digital giants have thrived with the help of ready, lightly regulated access to personal information of all sorts. Others point out that there's plenty of evidence that tighter regulation has put smaller European companies at a considerable disadvantage to deeper-pocketed U.S. rivals such as Google and Amazon. But the debate is entering a new phase. As companies increasingly embed artificial intelligence in their products, services, processes, and decision-making, attention is shifting to how data is used by the software--particularly by complex, evolving algorithms that might diagnose a cancer, drive a car, or approve a loan.

Now, it's also the latest company to seek an edge in artificial intelligence by making its own silicon chips. At a promotional event last month, Tesla revealed details of a custom AI chip called D1 for training the machine-learning algorithm behind its Autopilot self-driving system. The event focused on Tesla's AI work and featured a dancing human posing as a humanoid robot the company intends to build. Tesla is the latest nontraditional chipmaker to design its own silicon. As AI becomes more important and costly to deploy, other companies that are heavily invested in the technology--including Google, Amazon, and Microsoft--also now design their own chips.

Our mushy brains seem a far cry from the solid silicon chips in computer processors, but scientists have a long history of comparing the two. As Alan Turing put it in 1952: "We are not interested in the fact that the brain has the consistency of cold porridge." Today, the most powerful artificial intelligence systems employ a type of machine learning called deep learning. Their algorithms learn by processing massive amounts of data through hidden layers of interconnected nodes, referred to as deep neural networks. As their name suggests, deep neural networks were inspired by the real neural networks in the brain, with the nodes modeled after real neurons -- or, at least, after what neuroscientists knew about neurons back in the 1950s, when an influential neuron model called the perceptron was born.

Low-code platforms improve the speed and quality of developing applications, integrations, and data visualizations. Instead of building forms and workflows in code, low-code platforms provide drag-and-drop interfaces to design screens, workflows, and data visualizations used in web and mobile applications. Low-code integration tools support data integrations, data prep, API orchestrations, and connections to common SaaS platforms. If you're designing dashboards and reports, there are many low-code options to connect to data sources and create data visualizations. If you can do it in code, there's probably a low-code or no-code technology that can help accelerate the development process and simplify ongoing maintenance.

Everyone is talking about artificial intelligence (AI) and machine learning, and some people in the legal field are already taking advantage of these technological capabilities. However, the extraordinary progress in legal AI technology has some lawyers worried about their prospects in their chosen profession, fearing that AI will soon replace them. This fear is unfounded because it is challenging for AI and machine learning technology to replace the job of a legal professional. On the contrary, technology enables growth and productivity since it increases accuracy, making legal work more efficient. AI algorithms can transform several tasks, offering excellent corporate compliance, contract management, discovery, and due diligence.